SERS-Based System Used to Detect Synthetic Antioxidants

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Chinese scientists recently made a surface-enhanced Raman scattering (SERS)-based approach to detect synthetic antioxidants in food samples.

In a recent study out of Zhejiang University of Technology in Hangzhou, China, a new system based around surface-enhanced Raman scattering (SERS) was used to quantitatively detect synthetic antioxidants in food samples. Their findings were later published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy (1).

Packaging of Omega 3 capsules on a white textural background. Fish oil, tablets, capsules and vitamins, food supplement for cosmetic purposes or strengthening the body's immunity. healthcare concept | Image Credit: © Avocado_studio - stock.adobe.com

Packaging of Omega 3 capsules on a white textural background. Fish oil, tablets, capsules and vitamins, food supplement for cosmetic purposes or strengthening the body's immunity. healthcare concept | Image Credit: © Avocado_studio - stock.adobe.com

Synthetic antioxidants, which are phenolic compounds such as butylated hydroxyanisole (BHA), tert-butylhydroquinone (TBHQ), propyl gallate (PG), can inhibit or eliminate oxidative reactions involving oxygen. These reactions are typically utilized in industries such as food and pharmaceuticals due to outstanding effectiveness, high stability, and affordable prices. However, recent discoveries have showed that some chemically synthesized antioxidants exhibit certain levels of toxicity, which can affect the activity of human respiratory enzymes; this can lead to endocrine disruption, reproductive disorders, and even posing risks of teratogenicity and carcinogenicity, which has caused concerns among the public.

Read More: Revolutionizing Diagnostics: Machine Learning Unleashes the Power of Surface-Enhanced Raman Spectroscopy

There are various methods used across the world for detecting antioxidants, including high-performance liquid chromatography (HPLC), electrochemical analysis, gas chromatography–mass spectrometry (GC–MS), and more. However, these methods, while being effective, face challenges such as complex sample preparation and lengthy analysis times. As such, the scientists in this study used surface-enhanced Raman scattering (SERS) as a rapid and sensitive technique for detecting antioxidants. This technique was chosen for its unique fingerprint information. However, there is limited literature on this approach, and the direct adsorption of antioxidants onto conventional SERS substrates can be difficult because of phenolic hydroxyl groups, which results in weak Raman scattering signals.

Read More: SERS Used to Detect Early-Stage Liver Cancer

In this study, the scientists used a diazo derivatization reaction to enhance SERS signals by converting antioxidant molecules into azo derivatives; this would enable the amplification of weak Raman scattering signals through strong vibrational modes induced by the N=N double bond. The diazo derivatives stemming from this were then characterized using UV–visible absorption and infrared spectroscopy, which confirmed the occurrence of diazo derivatization of the antioxidants.

Additionally, the proposed method successfully achieved the rapid detection of three commonly used synthetic antioxidants, butylated hydroxyanisole (BHA), tert-butylhydroquinone (TBHQ), and propyl gallate (PG), by the application of interfacial self-assembled gold nanoparticles. By integrating a convolutional neural network (CNN) model, rapid predictions of BHA, PG, and TBHQ within the concentration range of 1 × 10-6 to 2 × 10-3 mol/L were achieved. The model’s predictive range surpassed the traditional quantitative method of manually selecting characteristic peaks, with linear coefficients (R2) of 0.9992, 0.9997, and 0.9997, respectively. Antioxidant recovery in real soybean oil samples ranged from 73.0% to 126.4%. Overall, the proposed SERS method eliminates the need for complex substrates while enabling the analysis and determination of synthetic antioxidants in edible oils within 20 minutes. This approach will supposedly provide a more convenient analytical approach for quality control in the food industry.

Reference

(1) Li, W.; Chen, Y.; Li, X.; Zhong, Y.; Xu, P.; Teng, Y. Ultrasensitive SERS Quantitative Detection of Antioxidants Via Diazo Derivatization Reaction and Deep Learning for Signal Fluctuation Mitigation. Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 2024, 313, 124086. DOI: 10.1016/j.saa.2024.124086

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